geom_point

Aesthetics

Let’s explore how aesthetics change meaning and clarity.

geom_point

Simple Geom

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

eg1 <- data.frame(
  democracy = c(-8, -7, -5, -3, 0, 2, 5, 8, 9),   # democracy score
  gdp = c(2, 9, 4, 7, 8, 20, 15, 25, 27)  # GDP per capita in $1,000s
)

geom_point

Simple Geom

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

geom_point

Simple Geom

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

# Scatterplot with geom_point
ggplot(data=eg1, 
       aes(x = democracy, y = gdp)) +
  geom_point()

geom_point

color(discrete)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

Let’s imagine that we have a new dataset with more variables:

geom_point

color(discrete)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

This is how we can emphasize the region (discrete):

# 1) color (discrete): distinguish categories with distinct hues
ggplot(eg1, aes(x = democracy, y = gdp, color = region)) +
  geom_point()

geom_point

color(continuous)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

This is how we can emphasize the corruption levels (continuous):

# 2) color (continuous): encode a numeric gradient (low → high)
ggplot(eg1, aes(x = democracy, y = gdp, color = corruption)) +
  geom_point()

geom_point

size(discrete)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

This is how we can emphasize the income groups (discrete):

# 3) size (discrete): High income = large points
ggplot(eg1, aes(x = democracy, y = gdp, size = income_group)) +
  geom_point()

geom_point

size(continuous)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

This is how we can emphasize population size (continuous)

# 4) size (continuous): encode magnitude (e.g., population)
ggplot(eg1, aes(x = democracy, y = gdp, size = population)) +
  geom_point()

geom_point

fill(discrete)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

This is how we can emphasize income groups (discrete).

# 5) fill (discrete): categories on filled shapes (pch 21–25, bars, areas)
ggplot(eg1, aes(x = democracy, y = gdp, fill = income_group)) +
  geom_point(shape = 21, color = "black")

geom_point

fill(continuous)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

This is how we can emphasize the level of corruption (continuous).

# 6) fill (continuous): numeric gradient on filled shapes
ggplot(eg1, aes(x = democracy, y = gdp, fill = corruption)) +
  geom_point(shape = 21, color = "black")

geom_point

shape(discrete)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

# 7) shape (discrete): differentiate categories; limited distinct shapes
ggplot(eg1, aes(x = democracy, y = gdp, shape = region)) +
  geom_point()

geom_point

alpha(discrete)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

# 8) alpha (discrete): subtle group differences; use sparingly in legends
ggplot(eg1, aes(x = democracy, y = gdp, alpha = region)) +
  geom_point()

geom_point

alpha(continuous)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

# 9) alpha (continuous): opacity encodes intensity (higher → more opaque)
ggplot(eg1, aes(x = democracy, y = gdp, alpha = corruption)) +
  geom_point()

geom_point

multiple aesthetics (readable)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

# 10) Combine judiciously: color = region (discrete), size = population (continuous)
ggplot(eg1, aes(x = democracy, y = gdp)) +
  geom_point(aes(color = region, size = population))

geom_point

too many aesthetics (overloaded)

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

# Overloaded example (hard to read): too many mapped aesthetics at once
# Prefer ≤2 mappings; prioritize the question your plot answers
ggplot(eg1, aes(x = democracy, y = gdp)) +
  geom_point(
    aes(color = region,
        fill  = income_group,
        shape = region,
        size  = population,
        alpha = corruption)
  )

Mapping vs. Setting

Key Idea

• Mapping: aesthetic varies with data → put inside aes()
• Setting: aesthetic is fixed → put outside aes()

This is how we create the data:

eg2 <- data.frame(
  x = 1:5,
  y = c(2,4,6,8,10),
  group = c("A","B","A","B","A")
)

Mapping vs. Setting

Key Idea

• Mapping: aesthetic varies with data → put inside aes()
• Setting: aesthetic is fixed → put outside aes()

This is what it looks like:

Mapping vs. Setting

Key Idea

• Mapping: aesthetic varies with data → put inside aes()
• Setting: aesthetic is fixed → put outside aes()

This is the difference:

# Mapping: put inside aes() when the value comes from data
# Setting: put outside aes() for a constant value
ggplot(eg2, aes(x = x, y = y, color = group)) +
  geom_point()+
  labs(title = "Mapping: color depends on `group`")

# Mapping: put inside aes() when the value comes from data
# Setting: put outside aes() for a constant value
ggplot(eg2, aes(x = x, y = y)) +
  geom_point(color = "red") +
  labs(title = "Setting: all points red")

Mapping vs. Setting

Common Mistake

# Wrong: constant inside aes()
ggplot(eg2, aes(x = x, y = y, color = "red")) +
  geom_point() +
  labs(title = "Mistake: constant mapped")

# Correct: constant outside aes()
ggplot(eg2, aes(x = x, y = y)) +
  geom_point(color = "red") +
  labs(title = "Correct: constant set")

Why it’s wrong:

• Left plot: "red" treated as a category, so ggplot makes a useless legend.
• Right plot: "red" is just a style — no legend clutter.

geom_col

Aesthetics

geom_point

Simple Geom

Suppose we have survey responses by education level. We’ll compare the number of respondents in each category.

# Pre-aggregated counts instead of raw rows
eg3 <- data.frame(
  education = c(
    "Primary", "Primary", "High School", "High School", "High School",
    "College", "College", "College", "College"
  )
)

geom_col

Simple Geom

Suppose we have survey responses by education level. We’ll compare the number of respondents in each category.

library(dplyr)
# Count occurrences of each education level
eg3b <- eg3 %>%
  count(education)

geom_col

Simple Geom

Suppose we have survey responses by education level. We’ll compare the number of respondents in each category.

ggplot(eg3b, aes(x = education, y = n)) +
  geom_col()

geom_col

fill(discrete)

Suppose we have survey responses by education level. We’ll compare the number of respondents in each category.

# fill = education (discrete)
ggplot(eg3b, aes(x = education, y = n, fill = education)) +
  geom_col()

geom_col

fill(discrete)

Suppose we have survey responses by education level. We’ll compare the number of respondents in each category.

# fill = education, color = black border
ggplot(eg3b, aes(x = education, y = n, fill = education)) +
  geom_col(color = "black", linewidth = 1)

geom_line

Aesthetics

geom_line

Simple Geom

Suppose we have data on average voter turnout (%) in national elections over several years. We want to see the trend in participation.

# Toy dataset
eg4 <- data.frame(
  year = c(2000, 2004, 2008, 2012, 2016, 2020),
  turnout = c(55, 58, 62, 60, 59, 65)
)

geom_line

Simple Geom

Suppose we have data on average voter turnout (%) in national elections over several years. We want to see the trend in participation.

ggplot(eg4, aes(x = year, y = turnout)) +
  geom_line()

geom_line

color(discrete)

Suppose we have data on average voter turnout (%) in national elections over several years for the US and the UK. We want to see the trend in participation.

# Toy dataset with US and UK
eg5 <- data.frame(
  year = rep(c(2000, 2004, 2008, 2012, 2016, 2020), times = 2),
  turnout = c(
    # US presidential elections
    54, 60, 62, 58, 56, 65,
    # UK general elections (closest years aligned to US election years for teaching)
    59, 61, 65, 66, 68, 67
  ),
  country = rep(c("United States", "United Kingdom"), each = 6)
)

geom_line

color(discrete)

Suppose we have data on average voter turnout (%) in national elections over several years for the US and the UK. We want to see the trend in participation.

geom_line

color(discrete)

Suppose we have data on average voter turnout (%) in national elections over several years for the US and the UK. We want to see the trend in participation.

ggplot(eg5, aes(x = year, y = turnout, color = country)) +
  geom_line()

geom_line

linewidth(discrete)

Suppose we have data on average voter turnout (%) in national elections over several years for the US and the UK. We want to see the trend in participation.

ggplot(eg5, aes(x = year, y = turnout, linewidth = country)) +
  geom_line()

geom_line

linetype(discrete)

Suppose we have data on average voter turnout (%) in national elections over several years for the US and the UK. We want to see the trend in participation.

ggplot(eg5, aes(x = year, y = turnout, linetype = country)) +
  geom_line()

geom_line

Line Types and Color

Suppose we have data on average voter turnout (%) in national elections over several years for the US and the UK. We want to see the trend in participation.

ggplot(eg5, aes(x = year, y = turnout, 
      linetype = country, color = country)) +
  geom_line()

geom_line

Line Types, Color, and Points

Suppose we have data on average voter turnout (%) in national elections over several years for the US and the UK. We want to see the trend in participation.

ggplot(eg5, aes(x = year, y = turnout, 
  linetype = country, color = country)) +
  geom_line() +
  geom_point()

Complex Geom Aesthetics

Complex Geoms

geom_boxplot

Suppose we surveyed people about their trust in government on a 1–10 scale (1 = no trust, 10 = complete trust). We want to compare typical values and how spread out the answers are for men and women.

set.seed(123)

# Toy survey dataset
eg6 <- data.frame(
  gender = rep(c("Men", "Women"), each = 20),
  trust = c(
    rnorm(20, mean = 5, sd = 2),
    rnorm(20, mean = 8, sd = 1)
  )
)

Complex Geoms

geom_boxplot

Suppose we surveyed people about their trust in government on a 1–10 scale (1 = no trust, 10 = complete trust). We want to compare typical values and how spread out the answers are for men and women.

Complex Geoms

geom_boxplot

Suppose we surveyed people about their trust in government on a 1–10 scale (1 = no trust, 10 = complete trust). We want to compare typical values and how spread out the answers are for men and women.

ggplot(eg6, aes(x = gender, y = trust)) +
  geom_boxplot()

Complex Geoms

geom_boxplot: fill(color)

Suppose we surveyed people about their trust in government on a 1–10 scale (1 = no trust, 10 = complete trust). We want to compare typical values and how spread out the answers are for men and women.

ggplot(eg6, aes(x = gender, y = trust, fill = gender)) +
  geom_boxplot()

Complex Geoms

geom_histogram: fill + alpha

Suppose we surveyed people about their trust in government on a 1–10 scale (1 = no trust, 10 = complete trust). We want to compare typical values and how spread out the answers are for men and women.

ggplot(eg6, aes(x = trust, fill = gender)) +
  geom_histogram(bins = 10, color = "white", alpha = 0.5, position = "identity")

Complex Geoms

geom_sf fill(color)

library(ggplot2)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)

world <- ne_countries(scale = "medium", 
                      returnclass = "sf")
europe_bounds <- list(x = c(-10, 40),
                      y = c(35, 70))

# Mapping it
ggplot() +
  geom_sf(data = world, aes(fill=log(pop_est))) +
  coord_sf(xlim = europe_bounds$x, 
           ylim = europe_bounds$y)

Colors

On the most fundamental level, we need to use the right colors for our visualizations

This is relevant for:

Color Statistics

8% of men and 0.5% of women have some form of color blindness

Thus, colors should be distinguishable by people with different forms of color blindness

Color Contrasts

The Viridis palette in R allows us to create color-blind-friendly graphs

These are predefined palettes that are widely used.

geom_point

color(discrete) - No Viridis

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

Let’s imagine that we have a new dataset with more variables:

geom_point

color(discrete) - No Viridis

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

# 1) color (discrete): differentiate categories with distinct hues
ggplot(eg1, aes(x = democracy, y = gdp, color = region)) +
  geom_point()

geom_point

color(discrete) - Viridis

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

# 1) color (discrete): differentiate categories with distinct hues
ggplot(eg1, aes(x = democracy, y = gdp, color = region)) +
  geom_point()+
  scale_color_viridis_d(option = "viridis")  # you can also try "magma", "inferno", "cividis", etc.

geom_point

color(continuous) - No Viridis

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

# 2) color (continuous): encode a numeric gradient (low → high)
ggplot(eg1, aes(x = democracy, y = gdp, color = corruption)) +
  geom_point()

geom_point

color(continuous) - Viridis

Imagine we have data about 9 countries that record their level of democracy from -10 to +10 (x-axis) and their GDP per capita in $1,000s (y-axis).

# 2) color (continuous): encode a numeric gradient (low → high)
ggplot(eg1, aes(x = democracy, y = gdp, color = corruption)) +
  geom_point()+
  scale_color_viridis_c(option = "viridis")  # you can also try "magma", "inferno", "cividis", etc.

Complex Geoms

geom_sf fill(color) - No Viridis

library(ggplot2)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)

world <- ne_countries(scale = "medium", 
                      returnclass = "sf")
europe_bounds <- list(x = c(-10, 40),
                      y = c(35, 70))

# Mapping it
ggplot() +
  geom_sf(data = world, aes(fill=log(pop_est))) +
  coord_sf(xlim = europe_bounds$x, 
           ylim = europe_bounds$y) +
  labs(title = "European Countries")

Complex Geoms

geom_sf fill(color) - Viridis

library(ggplot2)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)

world <- ne_countries(scale = "medium", 
                      returnclass = "sf")
europe_bounds <- list(x = c(-10, 40),
                      y = c(35, 70))

# Mapping it
ggplot() +
  geom_sf(data = world, aes(fill=log(pop_est))) +
  coord_sf(xlim = europe_bounds$x, 
           ylim = europe_bounds$y) +
  labs(title = "European Countries")+
  scale_fill_viridis_c(option = "viridis")  # you can also try "magma", "inferno", "cividis", etc.

Conclusion

• Geoms provide the structure; aesthetics add meaning
• Use aesthetics (color, size, shape, alpha, linetype) to highlight patterns
• Remember: mapping = variable-driven | setting = fixed value
• Aesthetics can clarify or confuse — use them thoughtfully
• Goal: craft plots that are clear, engaging, and persuasive

geom_point

color(continuous)

What do you see?

ggplot(eg1, aes(x = democracy, y = gdp)) +
  geom_point()

geom_point

color(continuous)

How does the story change?

# 2) color (continuous): encode a numeric gradient (low → high)
ggplot(eg1, aes(x = democracy, y = gdp, color=corruption)) +
  geom_point() +
  scale_color_viridis_c(option = "viridis")  # you can also try "magma", "inferno", "cividis", etc.

geom_point

size(continuous)

What does adding size tell us?

# color (continuous): encode a numeric gradient (low → high)
# size (continuous): encode magnitude with point size
ggplot(eg1, aes(x = democracy, y = gdp, color=corruption, size=population)) +
  geom_point() +
  scale_color_viridis_c(option = "viridis")  # you can also try "magma", "inferno", "cividis", etc.

geom_point

color(discrete)

What do you see?

ggplot(eg1, aes(x = democracy, y = gdp)) +
  geom_point()

geom_point

color(continuous)

How does the story change?

# 2) color (continuous): encode a numeric gradient (low → high)
ggplot(eg1, aes(x = democracy, y = gdp, color=corruption)) +
  geom_point() +
  scale_color_viridis_c(option = "viridis")  # you can also try "magma", "inferno", "cividis", etc.

geom_point

color(continuous)

What does adding size tell us?

# color (continuous): encode a numeric gradient (low → high)
# size (continuous): encode magnitude with point size
ggplot(eg1, aes(x = democracy, y = gdp, color=corruption, size=population)) +
  geom_point() +
  scale_color_viridis_c(option = "viridis")  # you can also try "magma", "inferno", "cividis", etc.